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Mappt & QGIS Workflow: Using Drones to Enrich Field Assessments

DEM Dubai Airport Satellite Imagery Digital Surface Map orthomosaic sentinel 2 multispectral bands satellite imaging corporation

When it comes to fieldwork, the best outcome (aside from zero injuries) is to return having collected as much high-quality data as possible. We all want to ensure our project goals are reached successfully, so using a wide variety of relevant data to address the project criteria is a good strategy. An example of this is producing high resolution Digital Surface Maps (DSMs) using drones to complement field surveys conducted on-foot. These DSMs can provide additional information such as elevation, NDVI indices and more to complement the field survey data. 

DEM Dubai Airport Satellite Imagery Digital Surface Map orthomosaic sentinel 2 multispectral bands satellite imaging corporation

Digital Surface Map of Dubai International Airport by Sat Imaging Corp.

The workflow below describes the process for creating a DSM with QGIS to produce a data-rich field map, which complements field survey operations conducted using Mappt.

The Mappt team conducted a field survey at Kensington Bushland, in which we collected a range of points to characterise the local vegetation using Mappt.

Kensington Bushland, City of Victoria Park, walk leisure recreation natural park for

Kensington Bushland, City of Victoria Park

Each survey point contained a range of attributes for the field worker to fill out on location, including plant species, condition, time, % coverage etc. We created the attribute form using Mappt’s handy drop-down feature to collect all the field data. This was of course put together and pre-loaded prior to heading out into the field, like the intelligent and efficient field workers we are 🙂

(Click HERE for a free copy of our Ultimate Field Checklist)

Of most interest for this assessment was the species and condition of vegetation at the field site. The project goal was to explore trends that may explain any gradients in the species structure, coverage and/or condition. We collected additional drone imagery over the survey area, which was used to provide valuable complementary information on the vegetation community.

 

Now that we’ve returned from the field in good spirits and relatively unscathed, it’s time to begin our workflow process for the field survey!

Loading Mappt data into QGIS

First, we want to export the survey data collected using Mappt and bring it into QGIS. To do this, we want to navigate to the saved project file within Mappt and then click the export icon. We then want to select all layers, choose the GeoJSON format for QGIS, then choose to export to external apps, lastly selecting Google Drive as the export location. (See below)

Mappt user interface field mapping collecting georeferenced images for vegetation assessment screenshot_20190605-110141

That’s it, put down the tablets people! You’ve successfully collected and exported your field data from Mappt. Pretty easy right?

Now we want to move over to our desktop computer and load the Mappt data in for further processing in QGIS. Open up a new project in QGIS and check that the CRS projection is set to WGS 84 (under Project > Properties).

Next, add Google Satellite as a base layer for your project (click on the Web > QuickMapServices > Search QMS, then click on Google Satellite in the window that opens in the bottom-right).

 data-source-manager-icon

Click this icon to Import the Mappt data from Google Drive into QGIS through the Data Source Manager.

 

 

Now double-click on the Vector file in the Layer Window to make any style changes desired. For my data, I have characterised survey points into species type and given them different colours. I also filled in the polygon for the reserve area, and indicated my survey entry and exit points with coloured lines.

mapptdata

Now you’ve got your Mappt survey data looking schmick in QGIS, it’s time to bring in the drone images to create a DSM overlay! First, we will need to combine all the photos from your drone together into the one orthomosaic (to rule them all).

Creating a Digital Surface Map using Drone Deploy

Go to http://dronedeploy.com and create an account if this is your first time using it. Then simply upload all your images into the window and drone deploy will create an orthomosaic for you! You can change the processing time by toggling the speed vs quality bar under the ‘Advanced’ tab. Click ‘Upload Images’ to begin the process.

It might be time for a tea break now, as this does take a while.

drone deploy mapping software online orthomosaic creator drone imagery

Once the map has finished processing, you have the option to export the orthomosaic as a natural colour GeoTIFF, as well as NDVI index and Elevation map. Export any that you want and ensure they are GeoTIFFs.

Now, we want to bring QGIS back up and load in the files, once again using the Data Source Manager.

raster-icon Click this icon within Data Source Manager to load the GeoTIFFs as raster files.

Again, we can change the opacity and style of each layer to get the desired style. For my orthomosaic, I chose to reduce the opacity of the natural colour layer so that the elevation can be seen.

orthomosaic-data

There are some interesting features of the elevation that seem to overlap with some patterns in the vegetation structure! We should create a map to show the boss.

Creating a map in QGIS including Mappt survey data and DSM data

print-layout-icon To do this, we want to click on the Print Layout icon in QGIS.

This opens a blank page from which we can begin to draw our map.

addmap-iconIn the Composition Window that’s just opened, click the ‘Add Map’ icon.

Then click and drag an area over the canvas in the window to produce a map. The map produced is based on the view in your main QGIS window, so you may need to do some final style tweaks to finalise the image.

map-icons You can then add a Title, Legend and Scale bar to your map using their respective icons.

You can customise all of these to your liking by clicking on the feature then using the ‘Item Properties’ window on the right to adjust the information displayed.

imageicon Next, add a North Arrow by first clicking on the ‘Add Image’ icon.

Then, navigate over to the ‘Item Properties’ window and click on the ‘Search Directories’ drop down. Here you will find a number of images that are suitable as a North Arrow.

Lastly, click on the map itself and navigate through the item properties until you find the ‘Grids’ drop down (See below). Click on the green plus icon to add a grid, then click Modify Grid to set the scale. Once your grid is displayed nicely, lastly change the frame style to ‘Zebra’ and then close out.

grid

Voila! Your map is now complete for reporting. For my data I’ve found a pattern between increasing elevation on my DSM, and abundance of Banksia menziesii. Neat!

QGIS map of kensington bushland created using mappt and drones to produce digital surface map and vegetation survey data

Using Mappt to Collect Data in the Field, Part 3 of 3

This is the final installment in our 3-part series, which has been offering a simple user story that could form a workflow basis to be adopted by new or existing Mappt users.

If you haven’t read them, take a look at part 1, “Preparation,” here and part 2, “In the Field,” here.  Part 1 dealt with preparing your tablets and datasets, while part 2 covered importing, updating and exporting job data while in the field.

Returning to Base

Once you return to base, you will want to get your captured data off the tablet.

Within Mappt, this can be achieved by selecting a layer and then exporting the layer to email. Your tablet will then present a list of installed apps that will offer to transmit the data for you. This will include apps that are not email-centric, but are otherwise great options for sending data. For example, if you have Google Drive or other cloud-based storage solution installed, you will be able to upload your data there.

Google Drive (and other cloud-based storage apps) provide great ways to collaborate on data and combine datasets.

Another option is to simply email the data, perhaps to a team leader or other staff member responsible for coordinating data changes.

Screenshot of the Open From Google Drive button in Mappt

You can also export the data to a removable flash card and copy the files to the computer where you may have tools for integrating back into the project.

A final option is to plug your tablet into a computer via the USB cord and copy the files that way.

Integration

Integrating the data back into your project datasets is a matter of much greater discussion, involving concerns such as conflicts, merging, authority, etc. and will not be covered here.  As suggested above, this may be something that is handled by a nominated member of your team, using tools designed specially for this purpose.

Finally

We hope that the topics covered in this 3-part series have provided some tips on developing your own workflows.  Be sure to post your thoughts in the comments, as we love hearing user stories!

Using Mappt to Collect Data in the Field, Part 2 of 3

This is part 2 in a 3-part series that offers a simple User Story that could form the basis of a workflow to be adopted by new or existing Mappt users.

If you haven’t already, we suggest reading part 1, “Preparation” here, which discussed preparing your tablets and datasets for work in the field.

In the Field

When performing your duties in the field, you will import your Project Datasets into Mappt to assist in locating assets and referring to job information.  As suggested in phase 1, project datasets should not be edited; instead, data captured in the field should be logged into smaller, job template datasets.

When commencing a job, a new template should be imported and updated as the job progresses.

A good job template will allow you to easily capture new data while providing the structure necessary to capture quality, error-free data.  For Shapefiles, this would mean that the job template contains a pre-defined set of attributes, guiding the user to enter relevant data into the correct places.

If your work is conducted in an Internet-connected area, you may consider hosting and distributing your datasets over Google Drive via a shared folder.  Doing this will allow your administrative teams to provide consistently accurate datasets to your team, without the need to redistribute datasets via email or other manual methods.

Custom offline imagery can be loaded to assist with navigation in combination with the tablet’s GPS hardware. If your imagery is high-quality and correctly geo-referenced, you can use the imagery to position features on the map with a high degree of accuracy. This is perfect for when the GPS hardware is not accurate enough.

Thematic Mapping (previously known as Classifications) can be applied to larger datasets to locate features with certain attributes.  For example, consider a dataset containing markers that represent assets to be inspected, with an INSPECTED YES/NO attribute, indicating whether the asset requires inspection.  Using Thematic Mapping, the markers could be styled green to highlight the markers to be inspected, providing an easy method to visually indicate work to be done.

thematic mapping

Completing the Job

Once data has been obtained and the job complete, any captured data layers can be exported to the local storage of the tablet before moving on to the next job.

In phase 3, we will outline some options for retrieving the recorded data from the tablet for re-integration back into the project.

Using Mappt to Collect Data in the Field, Part 1 of 3

In the Mappt cave we are always interested in the workflows and procedures our customers employ when using Mappt in the field.

We find that most of these workflows consist of the same base necessities, regardless of the industry-specific nature of the work.

Over the next couple of posts, we will outline a simple 3-phase User Story that could form the basis of a workflow to be adopted by new or existing Mappt users. This workflow is presented from the perspective of managing a small team of Mappt users for a particular project, but applies equally to one Mappt user performing a single job.

The phases are:

  • Preparation
  • In the Field
  • Returning to Base

This post will cover phase 1, “Preparation,” with posts for phase 2 and 3 coming in the following weeks.

Preparation

The Preparation Phase would involve gathering the relevant datasets and imagery to be deployed to the tablets. Ideally, the datasets and other files created during this phase would be deployed to the tablets just once, and remain relevant for the duration of the project.

One suggestion is to categorise your data into these groups:

Project Datasets

Project datasets (Shapefiles, ECW files, etc) that are relevant to the project as a whole. For example, a dataset for a project involving inspection parks may contain a spatial database of all parks to be inspected throughout the course of the project.

Screenshot showing an example Project Dataset

In this example, the Project Dataset consists of all of the parks to be inspected by field staff.

Job Templates

Job Templates are generally near-empty Shapefiles or Mappt Project files whose main purpose is to act as a template for data entry. This allows data to be collected in a uniform way, which is then exported on a per-job basis for re-integration into project datasets back at base.

Screenshot showing an example Job Template.

Note that as in the example above, Job Template Shapefiles must have at least one dummy feature defined; an empty Shapefile can not exist.  Once imported into Mappt, this dummy feature can be deleted.

Summary

The focus in the Preparation Phase is to create datasets that will require minimal manual handling or administrative intervention once the project commences. These datasets are deployed to tablets before the tablets are issued to staff.

This is especially useful for projects that require long-term disconnection from the Internet, whereby all relevant datasets can be copied to the device back at base, before transporting the tablets to the remote location.

Tune in next week for Phase 2: In the Field